LivingGLP1
Pairwise meta-analysis

Weight loss of 5 percent or more

Type 2 diabetes (adults); background therapy: one or more non insulin glucose lowering drugs

weeks 20 to 40 (target week 26)While on treatmentEach dose separatelyOdds ratio
3
Randomized trials
2
Treatments
1,683
Participants
202
Events

In plain language

Across 3 trials with 1,683 participants, 1 of 1 treatment had a clearly better result than placebo on weight loss of 5 percent or more. The most favorable estimate was for Lixisenatide 20 ug subcutaneous once daily: odds ratio 1.79 (95% credible interval 1.10 to 3.00).

Higher values favor the treatment. "Clearly" means the 95% credible interval excludes no difference. These are averages across trials; they do not say how any one person will respond. Not medical advice.

Network of trials

Each circle is a treatment; each line joins treatments that were randomized against each other in at least one trial.
LIXI_SC_20UG_QDPLACEBO
Node area shows participants; line width shows trials comparing two treatments directly. Blue: GLP-1 receptor agonist containing treatments; grey: comparators. Hover over or tab to a node or line for details.

Effects compared with Placebo

Odds ratio. Higher values favor the treatment.
124← favors referencefavors treatment →Lixisenatide 20 ug subcutaneous …
Versus Placebo, log scale. Blue: Bayesian random effects posterior median with 95% credible interval (thick) and 95% prediction interval (dashed). Amber diamond: frequentist estimate with 95% confidence interval. Hover over or tab to a row for its values.
Effects of each treatment versus Placebo
TreatmentBayesian OR (95% CrI)95% prediction intervalFrequentist OR (95% CI)Verdict
Lixisenatide 20 ug subcutaneous once daily
LIXI_SC_20UG_QD
1.79 (1.10 to 3.00)0.75 to 4.121.80 (1.28 to 2.52)Better

Ranking

How likely each treatment is to be among the best. Rankings ignore the size of differences and their certainty, so read them with the effects above.

Heterogeneity, fit and convergence

Bayesian random effects (primary)

Heterogeneity (tau)
0.231 (95% CrI 0.062 to 0.766), log scale
Residual deviance
5.3 for 6 data points
DIC
10.2 (pD 4.9)
Largest R-hat
1.001
Smallest ESS
bulk 3,230, tail 3,987
Divergent transitions
0
Convergence
All criteria met
Priors
PR02 N(0, 5^2); PR05 N(0, 2.5^2); PR09 log normal(-2.29, 1.53^2) on tau^2 (General physical health indicators, pharm vs placebo control)

Frequentist check (netmeta)

Method
inverse variance, REML, OR
Tau
0.000
I squared
0%
Q (total)
1.26 on 2 df, P = 0.533

Inconsistency

Do trials comparing two treatments directly agree with the indirect evidence through other treatments?

No comparison here has both direct and indirect evidence, so inconsistency cannot be checked by node splitting.

Small study effects

A comparison adjusted funnel plot: each point is one trial comparison, centered on its own network estimate. Asymmetry can signal that small trials with unfavorable results are missing.

Fewer than ten trial comparisons, too few to judge funnel plot asymmetry.

Baseline characteristics of the trials

The baseline value of each effect modifier or prognostic factor that applies here, per trial: mean or percent over arms, weighted by the number randomized. A note under a value names a stand in (a median for a mean, 100 minus the male share, Asian subgroup rows, an all Asian site list); "not reported" means no source of the trial prints it.
Baseline effect modifiers and prognostic factors per trial
TrialBaseline BMI or body weight (kg/m2)
effect modifier, prognostic factor; 2 of 3
Sex (percent female)
effect modifier, prognostic factor; 3 of 3
Treatment duration (weeks)
effect modifier; 3 of 3
Lifestyle co-intervention intensity (none standard intensive)
effect modifier, prognostic factor; 3 of 3
GETGOAL-S
S000491
body weight 83.0 kg
49.4824standard
GetGoal-M-Asia
S001185
26.9150.6424not_reported
GETGOAL-P
S001204
33.9247.5224not_reported

Comparability of the trials

Checked across designs (the set of treatments each trial compares) for eligibility, outcome definition and assessment, and the baseline factors above. Flags point to possible intransitivity; notes describe variation that is not tied to design.
  • FlagBaseline, Baseline BMI or body weight: not reported by 1 of 3 trials
  • NoteOutcome assessment, intercurrent events (discontinuation): mixed: not stated 2, while_on_treatment 1
  • NoteData handling, input conversions: count_from_percent 6
Comparability items per trial
Trialtime weeksanalysis setdefinitioninstrumentadjudicatedproportion methodintercurrent events (discontinuation)intercurrent events (rescue)blindingfundingstart yearEligibility
GETGOAL-S24modified_ittnot statednot statednot statednot_statednot statednot stateddouble_blindindustry2008adults on sulfonylurea with or without metformin; HbA1c 7 to 10 percent
GetGoal-M-Asia24modified_ittnot statednot statednot statednot_statedwhile_on_treatmentnot stateddouble_blindindustry2010Asian adults with type 2 diabetes on metformin with or without sulfonylurea
GETGOAL-P24modified_ittnot statednot statednot statednot_statednot statednot stateddouble_blindindustry2008type 2 diabetes on pioglitazone 30 mg or more with or without metformin; HbA1c 7 percent or more

Trial data

What each trial contributes. Arm rows give the arm value with its standard error, or events over participants; contrast rows give the odds ratio on the analysis scale (log) against the trial's first row. Flags mark converted, combined or approximated inputs.
Trial data entering this analysis
TrialArmTreatmentWeekDataValueSEEvents / NFlagsSource
GETGOAL-SA1PLACEBO24arm, responders20 / 278count_from_percentregistry:outcomes
GETGOAL-SA2LIXI_SC_20UG_QD24arm, responders80 / 554count_from_percentregistry:outcomes
GetGoal-M-AsiaA1PLACEBO24arm, responders28 / 191count_from_percentregistry:outcomes
GetGoal-M-AsiaA2LIXI_SC_20UG_QD24arm, responders37 / 188count_from_percentregistry:outcomes
GETGOAL-PA1PLACEBO24arm, responders8 / 157count_from_percentregistry:outcomes
GETGOAL-PA2LIXI_SC_20UG_QD24arm, responders29 / 315count_from_percentregistry:outcomes

Rerun this analysis yourself

Remove treatments or trials and refit the frequentist and Bayesian models in your browser, with the same methods as the published analysis.

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